Published:Journal of Chromatographic Science,
ISSN 0021-9665 Volume 45,
Number 1, January 2007, pp.38-44
Optimization of a Stability-Indicating HPLC Method
for the Simultaneous Determination of Rifampicin, Isoniazid, and
Pyrazinamide in a Fixed-Dose Combination using Artificial Neural
Networks
B.D. Glass[1], S. Agatonovic-Kustrin[2], Y.-J.
Chen[3], and M.H. Wisch[3]
[1]School of Pharmacy and Molecular Sciences, James Cook University,
Townsville, QLD 4811, Australia;
[2]Pharmacy, School of Biomedical, Bimolecular and Chemical Sciences,
M315, The University of Western Australia, Crawley, WA 6009, Australia;
and
[3]Faculty of Pharmacy and Department of Chemistry, Rhodes University,
Grahamstown 6140, South Africa
The aim of this study is to develop and optimize
a simple and reliable high-performance liquid chromatography (HPLC)
method for the simultaneous determination of rifampicin (RIF),
isoniazid (INH), and pyrazinamide (PZA) in a fixed-dose combination.
The method is developed and optimized using an artificial neural
network (ANN) for data modeling. Retention times under different
experimental conditions (solvent, buffer type, and pH) and using
four different column types (referred to as the input and testing
data) are used to train, validate, and test the ANN model. The
developed model is then used to maximize HPLC performance by optimizing
separation. The sensitivity of the separation (retention time)
to the changes in column type, concentration, and type of solvent
and buffer in the mobile phase are investigated. Acetonitrile
(ACN) as a solvent and tetrabutylammonium hydroxide (tBAH), used
to adjust pH, have the greatest influence on the chromatographic
separation of PZA and INH and are used for the final optimization.
The best separation and reasonably short retention times are produced
on the µ-bondapak C18, 4.6 ¥ 250-mm column, 10 µm/125
Å using ACN–tBAH (42.5:57.5, v/v) (0.0002M) as the
mobile phase, and optimized at a final pH of 3.10.
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